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multi-objective Optimization of Perturbed Impulsive Rendezvous Trajectories Using Physical Programming

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2008年第6期31卷 1829-1832页

作者： Luo, Ya-zhong Tang, Guo-jin Parks, Geoff Natl Univ Def Technol Coll Aerosp & Mat Engn Changsha 410073 Hunan Peoples R China Univ Cambridge Dept Engn Engn Design Ctr Cambridge CB2 1PZ England

Employment of physical programming as the base method to develop an optimization approach for obtaining a tradeoff solution for multi-objective nonlinear perturbed rendezvous was investigated. The physical programming was developed by Messac initially as an effective multi-objective and design optimization way successfully applied in various areas of engineering and research operations. This design approach incorporated three objectives apart from consideration of trajectory perturbations with minimum propellant cost, minimum time of flight, and maximum trajectory safety. It was found that designer preferred solution, can be directly applied to a mission plan. Successful demonstration of the physical programming method for efficient generation of desired design solution of perturbed rendezvous trajectory problem with homing example. Its beneficial to apply physical programming method with heuristic optimization algorithms to complex spacecraft trajectory design problems.

关键词： Sequential Quadratic Programming Propellant Spacecraft Trajectory Optimization multi objective Genetic Algorithm Annealing Lambert Algorithm multi objective evolutionary algorithms Pareto Optimality Perigee Stochastic Optimization

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Three-dimensional multi-objective design optimization of a transonic compressor rotor

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JOURNAL OF PROPULSION AND POWER 2004年第3期20卷 559-565页

作者： Benini, E Univ Padua I-35131 Padua Italy

A method for transonic compressor multi-objective design optimization was developed and applied to the NASA rotor 37, a test case representative of complex three-dimensional viscous flow structures in transonic bladings. The optimization problem considered was to maximize the isentropic efficiency of the rotor and to maximize its pressure ratio at the design point, using a constraint on the mass flow rate. The three-dimensional Navier-Stokes code CFX-TASCflow(R) was used for the aerodynamic analysis of blade designs. The capability of the code was validated by comparing the computed results to experimental data available in the open literature from probe traverses up-and downstream or the rotor. A multi-objective evolutionary algorithm was used for handling the optimization problem that makes use of Pareto optimality concepts and implements a novel genetic diversity evaluation method to establish a criterion for fitness assignment. The optimal rotor configurations, which correspond to the maximum pressure ratio and maximum efficiency, were obtained and compared to the original design.

关键词： Transonic Compressors Mass Flow Rate CFD Analysis multi objective evolutionary algorithms Supersonic Flow Cylindrical Coordinates Pareto Optimality Mach Numbers Rotor Blades Airfoil Chord

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multi-objective Optimization of Helicopter Airfoils Using Surrogate-Assisted Memetic algorithms

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JOURNAL OF AIRCRAFT 2012年第2期49卷 375-383页

作者： Massaro, Andrea Benini, Ernesto Univ Padua I-35131 Padua Italy

An effective strategy for airfoil numerical optimizations is presented that deals with multi-objective and multipoint problems and is specifically designed for helicopter applications. This technique is tested on several realistic problems of airfoil optimization with the aim of improving their aerodynamic performance by searching for optimal shape. The procedure is based On a multi-objective surrogate-assisted memetic algorithm coupled to a Navier-Stokes solver. First, the peculiar features of the algorithm are described with particular attention to its advantages when compared with more traditional evolutionary or gradient-based algorithms. Finally, the results of the optimizations carried out using different operating conditions are presented;starting from the optimal Pareto fronts, several solutions are selected and compared in terms of shapes and performance.

关键词： multi objective evolutionary algorithms Helicopter Blade Thin Airfoil Navier Stokes Solver Aerodynamic Performance Numerical Optimization Aerodynamic Simulation Neural Networks Reynolds Averaged Navier Stokes Numerical Simulation

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Constraint Handling and multi-objective Methods for the Evolution of Interplanetary Trajectories

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2015年第4期38卷 792-799页

作者： Izzo, Dario Hennes, Daniel Riccardi, Annalisa ESA European Space Res & Technol Ctr NL-2201 AZ Noordwijk Netherlands

A study is conducted to demonstrate constraint handling and multi-objective methods for the evolution of interplanetary trajectories. The study is based on the methods developed during GTOC6 and few advanced evolutionary techniques able to evolve complex Jupiter capture options are proposed. The evolutionary process is set up to be able to deal with constraints, along with multiple objectives and show the use of advanced evolutionary techniques to effectively search the solution space in both cases. The investigations show how these techniques allow the evolution of solutions of use to trajectory designers for the complex trajectory case considered in this study where other approaches fail to find acceptable solutions in a reasonable time.

关键词： Spacecraft Trajectory Optimization Callisto multi objective evolutionary algorithms Cumulative Distribution Function Deep Space Maneuver Sequential Quadratic Programming Genetic Algorithm Trajectory Optimization Annealing Derivative Free Method

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multi-objective Hidden Genes Genetic Algorithm for multigravity-Assist Trajectory Optimization

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第7期45卷 1269-1285页

作者： Ellithy, Ahmed Abdelkhalik, Ossama Englander, Jacob Iowa State Univ Aerospace Engn Dept 1200 Howe Hall Ames IA 50011 USA Iowa State Univ Dept Aerosp Engn 1200 Howe Hall Ames IA 50011 USA Johns Hopkins Appl Phys Lab Space Explorat Sect 11101 Johns Hopkins Rd Laurel MD 20723 USA

In multigravity-assist trajectory optimization, the size of the design space is a variable itself. The objective functions are usually replete with local minima. This paper presents a multi-objective hidden genes genetic algorithm (MOHGGA) for trajectory optimization. The length of the chromosome is selected large enough to enable modeling a given maximum number of swing-bys and maximum number of deep space maneuvers (DSMs). Binary tags are appended to those genes that control the swing-bys and DSMs. These binary tags are used to remove/add swing-bys and DSMs to a trajectory solution, and hence enable optimization among solutions of different sizes (different topologies). The MOHGGA generates Pareto fronts that have solutions of, in general, different number of swing-bys, swing-by planets, launch and arrival dates, and number of DSMs. Two objectives are considered in this paper: the total mission cost and total time of flight. An elitist nondominated sorting genetic algorithm is used. Local optimization is conducted on one objective function, holding the other objective function constant, to further improve the resulting Pareto front. Numerical results of four benchmark test cases for missions to Mars, Jupiter, Saturn, and Mercury are presented. The results demonstrate the capability of MOHGGA in searching for optimal trajectory topologies while optimizing two objectives.

关键词： Hidden Genes Genetic algorithms Trajectory Optimization Neptune Deep Space Maneuver Pericenter multi objective evolutionary algorithms Interplanetary Trajectories Lambert Algorithm Cassini Mission MATLAB

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Parallel evolutionary multi-objective Optimization on Large, Heterogeneous Clusters: An Applications Perspective

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JOURNAL OF AEROSPACE COMPUTING INFORMATION AND COMMUNICATION 2008年第11期5卷 460-478页

作者： Reed, Patrick M. Kollat, Joshua B. Ferringer, Matthew P. Thompson, Timothy G. Penn State Univ Dept Civil Engn University Pk PA 16802 USA Grumman Aerosp Corp Performance Modeling & Anal Dept Chantilly VA 20151 USA

Real-world operational use of parallel multi-objective evolutionary algorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heterogeneous computing hardware. This study provides a cross-disciplinary collaborative effort to assess and adapt parallel multi-objective evolutionary algorithms for operational use in satellite constellation design using large dedicated clusters with heterogeneous processor speeds/architectures. A statistical, metric-based evaluation framework is used to demonstrate how time-continuation, asynchronous evolution, dynamic population sizing, and epsilon dominance archiving can be used to enhance both simple master-slave parallelization strategies and more complex multiple-population schemes. Results for a benchmark constellation design coverage problem show that simple master-slave schemes that exploit time-continuation are often sufficient and potentially superior to complex multiple-population schemes.

关键词： Heterogeneous Cluster Satellite Constellations multi objective evolutionary algorithms Constellations Computing Hardware High Performance Computing Cumulative Distribution Function Sensitivity Analysis Parallel Computing Genetic Algorithm

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Surrogate-Based multi-objective Aerothermodynamic Design Optimization of Hypersonic Spiked Bodies

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AIAA JOURNAL 2012年第4期50卷 797-810页

作者： Ahmed, M. Y. M. Qin, N. Univ Sheffield Dept Mech Engn Sheffield S1 3JD S Yorkshire England

In this work, a stability-constrained biobjective (aerodynamic drag and heating) design optimization for the hypersonic spiked bodies is conducted. The shape optimization includes both the forebody shape and the spike/aerodisk shape through a generic parametric representation of the geometry. The NSGA-II multi-objective algorithm is coupled with the kriging surrogates that are constructed based on numerical solutions of laminar VISCOUS flows around the geometries. The optimization reveals that the two objectives, minimum drag and minimum heating, are competing for spiked forebodies such that a set of Pareto front solutions are obtained. On the minimum-drag and maximum-aeroheating extreme, the optimal designs are characterized by highly blunt, nearly flat forebodies with the possibility of flow unsteadiness. On the other extreme, the optimal designs are commonly of slender forebodies with small bluntness. Between the two extremes, a variety of nondominant designs can be found.

关键词： Aerodynamic Heating Aerodynamic Coefficients Flow Conditions Shape Optimization Boundary Layer Transition Numerical Simulation No Slip Condition Optimization Algorithm FORTRAN multi objective evolutionary algorithms

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Efficient and accurate evolutionary multi-objective optimization paradigms for satellite constellation design

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JOURNAL OF SPACECRAFT AND ROCKETS 2007年第3期44卷 682-691页

作者： Ferringer, Matthew P. Clifton, Ronald S. Thompson, Timothy G. Aerosp Corp Syst Engn Div Chantilly VA 20151 USA

multi-objective evolutionary algorithms have been shown to be effective optimization tools to search the complex tradeoff spaces of satellite constellation design. Often, the metrics that make up the design tradeoff require lengthy function evaluation time, resulting in a decreased utility of serial multi-objective evolutionary algorithms. In this research, the authors implement two parallel processing multi-objective evolutionary algorithm paradigms, the master-slave and island models, on a heterogeneous system of processors and operating systems. The efficiency and effectiveness of each approach is studied in the context of a regional coverage design problem. The island scheme outperforms the master-slave model with respect to efficiency. A study of the search dynamics for each paradigm demonstrates that both reliably meet the goals of multi-objective optimization (progressing toward the Pareto-optimal front while maintaining a diverse set of solutions). A key conclusion of this research is that both paradigms provide excellent approximations of the true Pareto frontier using a single seed, and when combined across multiple trial runs, they find nearly the entire set of Pareto-optimal solutions.

关键词： Satellite Constellations multi objective evolutionary algorithms Operating Systems Pareto Frontier Constellations Message Passing Interface Local Area Networks Genetic Algorithm Right Ascension Parallel Computing Systems

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Development of multi-objective Six-Sigma Approach for Robust Design Optimization

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JOURNAL OF AEROSPACE COMPUTING INFORMATION AND COMMUNICATION 2008年第8期5卷 215-233页

作者： Shimoyama, Koji Oyama, Akira Fujii, Kozo Univ Tokyo Bunkyo Ku Tokyo 1138654 Japan Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Sagamihara Kanagawa 2298510 Japan

In this study, a new optimization approach for robust design, design for multi-objective six sigma, has been developed and applied to three robust optimization problems. The design for multi-objective six sigma builds on the ideas of design for six sigma, coupled with multiobjective evolutionary algorithm, for an enhanced capability to reveal tradeoff information considering both optimality and robustness of design. While design for six sigma requires careful input parameter setting, design for multi-objective six sigma needs no such prior tuning, plus it can reveal the tradeoff information in a single optimization run. Three robust optimization problems were taken as to demonstrate the capabilities of design for multiobjective six sigma. Results indicate that design for multi-objective six sigma has a more practical and more efficient capability than the design for six sigma to reveal tradeoff design information considering both optimality and robustness of design.

关键词： Robust Design multi objective Six Sigma multi objective evolutionary algorithms Aeroplane Freestream Mach Number Aerodynamic Performance CFD Simulation Lift to Drag Ratio Beam (Structures) Airfoil Chord

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Optimization Alternatives for Robust Model-based Design of Synthetic Biological Circuits

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IFAC-PapersOnLine 2016年第7期49卷 821-826页

作者： Boada, Y. Pitarch, J.L. Vignoni, A. Reynoso-Meza, G. Picó, J. Universitat Politècnica de Valencia Camino de Vera S/N Valencia46022 Spain Systems Engineering and Control Department Universidad de Valladolid C/ Real de Burgos S/N Valladolid47011 Spain Center for Systems Biology Dresden and the Max Planck Institute of Molecular Cell Biology and Genetics Pfotenhauer str. 108 Dresden01307 Germany Pontifícia Universidade Catolica do Parana Imaculada Conceião 1155 CuritibaPR80215-901 Brazil

Synthetic biology is reaching the situation where tuning devices by hand is no longer possible due to the complexity of the biological circuits being designed. Thus, mathematical models need to be used in order, not only to predict the behavior of the designed synthetic devices; but to help on the selection of the biological parts, i.e., guidelines for the experimental implementation. However, since uncertainties are inherent to biology, the desired dynamics for the circuit usually requires a trade-off among several goals. Hence, a multi-objective optimization design (MOOD) naturally arises to get a suitable parametrization (or range) of the required kinetic parameters to build a biological device with some desired properties. Biologists have classically addressed this problem by evaluating a set of random Monte Carlo simulations with parameters between an operation range. In this paper, we propose solving the MOOD by means of dynamic programming using both a global multi-objective evolutionary algorithm (MOEA) and a local gradient-based nonlinear programming (NLP) solver. The performance of both alternatives is then checked in the design of a well-known biological circuit: a genetic incoherent feed-forward loop showing adaptive behavior. © 2016

关键词： Dynamic programming Computer aided design Economic and social effects evolutionary algorithms Genetic programming Intelligent systems Kinetic parameters Monte Carlo methods multiobjective optimization Nonlinear programming Timing circuits Adaptive behavior Biological parts Feedforward loops Local gradients multi objective evolutionary algorithms Parametrizations Robust modeling Synthetic biology

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